Electromyography Signal-Based Gesture Recognition for Human-Machine Interaction in Real-Time Through Model Calibration

Keyphrases

• Electromyography Signal 100%
• Model Calibration 100%
• Signal Base 100%
• Deep Neural Network 100%
• Human-machine Interaction 100%
• Gesture Recognition 100%
• Classification Accuracy 66%
• Real-time Classification 66%
• Statistical Analysis 33%
• Time-varying Data 33%
• Multilayer Perceptron 33%
• Signal Processing 33%
• Learning Environment 33%
• Support Vector Machine 33%
• Single Model 33%
• Temporal Window 33%
• Learning Paradigm 33%
• Electromyography 33%
• Voting System 33%
• State-of-the-art Techniques 33%
• Random Forest 33%
• Pre-calibration 33%
• EMG Signal 33%
• Fist 33%
• EMG Data 33%
• Myo Armband 33%
• Pseudo-real-time 33%
• Forearm muscle Activity 33%
• Ensemble Voting 33%
• Post-learning 33%
• Gesture Performance 33%
• Classical Machine Learning Model 33%
• Window Algorithm 33%

Computer Science

• Model Calibration 100%
• Gesture Recognition 100%
• Human-Computer Interaction 100%
• Electromyography 100%
• Deep Neural Network 100%
• Classification Accuracy 66%
• Machine Learning 33%
• Learning System 33%
• Multilayer Perceptron 33%
• Support Vector Machine 33%
• Random Decision Forest 33%
• Temporal Window 33%
• Muscle Activity 33%